Modern computer systems provide access to a wide range of digital assets such as documents, webpages, images, multimedia content, and so forth. Given the vast quantity of digital assets that is accessible via a networked computer, a search engine is often used to identify and locate digital assets based on a search query that includes one or more terms or phrases. Search engines are typically configured to display a ranked listing of search results, the ranking being based on a perceived relevance to the search query, with the most relevant search results appearing more prominently in the listing. A wide variety of different ranking algorithms have been developed in an effort to quickly and accurately identify the digital assets that are most relevant to a search query. While such algorithms are often used to perform Internet-based searches, they are also used to locate and identify digital assets within a narrower search field, such as the assets stored on a particular network or managed by a particular content management system. Even within a narrower search field, a ranking algorithm that accurately identifies the most relevant search results is highly valuable.
Searching for digital images presents additional challenges that do not arise when searching for purely text-based assets. For example, it is often difficult to generate a search query that accurately targets a particular image that a user has in mind. Moreover, digital images are often not directly associated with descriptive textual content, thus making it difficult to evaluate the relevance of image search results and present such results to the user in a meaningful way. And along these lines, the results of a digital image search are often presented in the form of a collection of thumbnail images which can be cumbersome to review with a handheld device such as a smartphone or tablet computer. These factors can make searching for digital images a tedious and frustrating process. Existing image-based search engines locate and organize search results by relying on secondary factors such as tags stored in image metadata, textual content located near an image, a user's search history, a user's personal profile, a user's geolocation, or analysis of other images a user has curated. In some cases frequently executed search queries can be saved for future use.